TechCrunch 50 has celebrated RedBeacon as winner for this 2009 Chapter.
"What do you guys do?!" Well their allows consumers who need a service
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What does that mean? That means that this year, this big High Tech show
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What a nice finishing!
Here is the interview of Aaron Lee
Here is the interview of Yaron Binur, that makes you understand how entrepreneurship is more than just a job, a passion, anything: it involves more just than you...

If you wanted to perform a single run of a current model of the
explosion of a star on your home computer, it would take more than
three years just to download the data.In order
to do cutting-edge astrophysics research, scientists need a way to more
quickly compile, execute and especially visualize these incredibly
complex simulations.

These
days, many scientists generate quadrillions of data points that provide
the basis for visualizations of everything from supernovas to protein
structures—and they’re quickly overwhelming current computing
capabilities. Scientists at the U.S. Department of Energy's Argonne
National Laboratory are exploring other ways to speed up the process,
using a technique called software-based parallel volume rendering.

Volume
rendering is a technique that can be used to make sense of the billions
of tiny points of data collected from an X-ray, MRI, or a researcher’s
simulation. For example, bone is denser than muscle, so an MRI
measuring the densities of every square millimeter of your arm will
register the higher readings for the radius bone in your forearm.

Argonne scientists are trying to find better, quicker ways to form a recognizable image from all of these points of data.Equations
can be written to search for sudden density changes in the dataset that
might set bone apart from muscle, and researchers can create a picture
of the entire arm, with bone and muscle tissue in different colors.

“But
on the scale that we’re working, creating a movie would take a very
long time on your laptop—just rotating the image one degree could take
days,” said Mark Hereld, who leads the visualization and analysis
efforts at the Argonne Leadership Computing Facility.

First,
researchers divide the data among many processing cores so that they
can all work at once, a technique that’s called parallel computing. On
Argonne’s Blue Gene®/P supercomputer, 160,000 computing cores all work
together in parallel. Today’s typical laptop, by comparison, has two
cores.

Usually,
the supercomputer’s work stops once the data ha­s been gathered, and
the data is sent to a set of graphics processors (GPUs), which create
the final visualizations. But the driving commercial force behind
developing GPUs has been the video game industry, so GPUs aren’t always
well suited for scientific tasks. In addition, the sheer amount of data
that has to be transferred from location to location eats up valuable
time and disk space.

“It’s
so much data that we can’t easily ask all of the questions that we want
to ask: each new answer creates new questions and it just takes too
much time to move the data from one calculation to the next,” said
Hereld. “That drives us to look for better and more efficient ways to
organize our computational work.”

Argonne
researchers wanted to know if they could improve performance by
skipping the transfer to the GPUs and instead performing the
visualizations right there on the supercomputer. They tested the
technique on a set of astrophysics data and found that they could
indeed increase the efficiency of the operation.

“We
were able to scale up to large problem sizes of over 80 billion voxels
per time step and generated images up to 16 megapixels,” said Tom
Peterka, a postdoctoral appointee in Argonne’s Mathematics and Computer
Science Division.

Because
the Blue Gene/P's main processor can visualize data as they are
analyzed, Argonne's scientists can investigate physical, chemical, and
biological phenomena with much more spatial and temporal detail.

According to Hereld, this new visualization method could enhance research in a wide variety of disciplines.“In
astrophysics, studying how stars burn and explode pulls together all
kinds of physics: hydrodynamics, gravitational physics, nuclear
chemistry and energy transport,” he said. “Other models study the
migration of dangerous pollutants through complex structures in the
soil, to see where they’re likely to end up; or combustion in cars and
manufacturing plants—where fuel is consumed and whether it’s efficient.”

“Those
kinds of problems often lead to questions that are very complicated to
pose mathematically,” Hereld said. “But when you can simply watch a
star explode through visualization of the simulation, you can gain
insight that’s not available any other way.”

Argonne’s
work in advanced computing is supported by the Department of Energy’s
Office of Advanced Scientific Computing Research (ASCR).

The
U.S. Department of Energy's Argonne National Laboratory seeks solutions
to pressing national problems in science and technology. The nation’s
first national laboratory, Argonne conducts leading-edge basic and
applied scientific research in virtually every scientific discipline.
Argonne researchers work closely with researchers from hundreds of
companies, universities, and federal, state and municipal agencies to
help them solve their specific problems, advance America’s scientific
leadership and prepare the nation for a better future. With employees
from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science.

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